Making sure that the two sequencing runs are not too different from each other. In general it seems like Run2 had a higher proportion of reads pass filter but not more overall reads ## Mock community check Next we check that the Mock community is well covered in both runs. Mock1 is from Run1 and Mock2 is from Run2. Looks like there’s very low contamination of other sequences in the mock samples. However, there is some slight difference in the ordering of mock abundances, but this variation is similar to previous runs.
## # A tibble: 2 × 3
## # Groups: sample [2]
## sample Correct Wrong
## <chr> <dbl> <dbl>
## 1 KLF_Mock1 0.999 0.00110
## 2 KLF_Mock2 1.00 0.0000472
Now that mock is checked out, we can remove it from the data.
We will use decontam and the negative controls (both extraction negatives and PCR negatives) to filter out potential contaminant sequences by frequency of appearance in the negative controls.
##
## FALSE TRUE
## 11676 62
Below is the taxonomy of the ASVs that were identified as contaminants.
| Kingdom | Phylum | Class | Order | Family | Genus | Species | |
|---|---|---|---|---|---|---|---|
| ASV283 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Xanthobacteraceae | Bradyrhizobium | NA |
| ASV966 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Beijerinckiaceae | Bosea | NA |
| ASV752 | Bacteria | Proteobacteria | Alphaproteobacteria | Caulobacterales | Caulobacteraceae | Brevundimonas | albigilva/nasdae/vesicularis |
| ASV5290 | Bacteria | Actinobacteriota | Actinobacteria | Micrococcales | Micrococcaceae | Rothia | dentocariosa |
| ASV861 | Bacteria | Actinobacteriota | Actinobacteria | Corynebacteriales | Mycobacteriaceae | Mycobacterium | NA |
| ASV169 | Bacteria | Actinobacteriota | Actinobacteria | Corynebacteriales | Corynebacteriaceae | Lawsonella | clevelandensis |
| ASV1193 | Bacteria | Actinobacteriota | Actinobacteria | Corynebacteriales | Corynebacteriaceae | Corynebacterium | aurimucosum/pseudogenitalium/tuberculostearicum |
| ASV492 | Bacteria | Actinobacteriota | Actinobacteria | Propionibacteriales | Nocardioidaceae | Nocardioides | jensenii |
| ASV485 | Bacteria | Actinobacteriota | Actinobacteria | Propionibacteriales | Propionibacteriaceae | Cutibacterium | acnes/avidum |
| ASV630 | Bacteria | Deinococcota | Deinococci | Thermales | Thermaceae | Thermus | amyloliquefaciens/scotoductus |
| ASV874 | Bacteria | Cyanobacteria | Cyanobacteriia | Chloroplast | NA | NA | NA |
| ASV890 | Bacteria | Fusobacteriota | Fusobacteriia | Fusobacteriales | Leptotrichiaceae | Leptotrichia | shahii/wadei |
| ASV3528 | Bacteria | Fusobacteriota | Fusobacteriia | Fusobacteriales | Fusobacteriaceae | Fusobacterium | nucleatum |
| ASV2065 | Bacteria | Firmicutes | Clostridia | Clostridiales | Oxobacteraceae | Oxobacter | NA |
| ASV322 | Bacteria | Firmicutes | Clostridia | Clostridia_or | Hungateiclostridiaceae | Ruminiclostridium | NA |
| ASV2038 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Leuconostocaceae | Leuconostoc | NA |
| ASV51 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Lactobacillaceae | Lactobacillus | NA |
| ASV1457 | Bacteria | Firmicutes | Bacilli | Lactobacillales | Streptococcaceae | Streptococcus | anginosus/intermedius |
| ASV2851 | Bacteria | Firmicutes | Bacilli | Staphylococcales | Gemellaceae | Gemella | NA |
| ASV19 | Bacteria | Firmicutes | Bacilli | Staphylococcales | Staphylococcaceae | Staphylococcus | NA |
| ASV464 | Bacteria | Firmicutes | Bacilli | Bacillales | Planococcaceae | Planococcus | NA |
| ASV2251 | Bacteria | Firmicutes | Bacilli | Bacillales | Planococcaceae | Chungangia | NA |
| ASV74 | Bacteria | Firmicutes | Bacilli | Bacillales | Sporolactobacillaceae | Sporolactobacillus | NA |
| ASV379 | Bacteria | Firmicutes | Bacilli | Bacillales | Bacillaceae | NA | NA |
| ASV316 | Bacteria | Firmicutes | Bacilli | Bacillales | Sporolactobacillaceae | Terrilactibacillus | laevilacticus |
| ASV2326 | Bacteria | Firmicutes | Negativicutes | Veillonellales-Selenomonadales | Veillonellaceae | Veillonella | parvula |
| ASV4175 | Bacteria | Proteobacteria | NA | NA | NA | NA | NA |
| ASV1270 | Bacteria | Proteobacteria | Alphaproteobacteria | Rickettsiales | Mitochondria | NA | NA |
| ASV474 | Bacteria | Proteobacteria | Alphaproteobacteria | Rickettsiales | Mitochondria | NA | NA |
| ASV1350 | Bacteria | Proteobacteria | Alphaproteobacteria | Rickettsiales | Mitochondria | NA | NA |
| ASV1372 | Bacteria | Proteobacteria | Alphaproteobacteria | Rickettsiales | Mitochondria | NA | NA |
| ASV175 | Bacteria | Proteobacteria | Alphaproteobacteria | Rickettsiales | Mitochondria | NA | NA |
| ASV633 | Bacteria | Proteobacteria | Alphaproteobacteria | Rickettsiales | Mitochondria | NA | NA |
| ASV4363 | Bacteria | Bacteroidota | Bacteroidia | Chitinophagales | Chitinophagaceae | Vibrionimonas | NA |
| ASV138 | Bacteria | Bacteroidota | Bacteroidia | Flavobacteriales | Weeksellaceae | Cloacibacterium | caeni/normanense/rupense |
| ASV4176 | Bacteria | Bacteroidota | Bacteroidia | Flavobacteriales | Flavobacteriaceae | Zunongwangia | NA |
| ASV916 | Bacteria | Bacteroidota | Bacteroidia | Chitinophagales | Saprospiraceae | Portibacter | NA |
| ASV4268 | Bacteria | Proteobacteria | Gammaproteobacteria | Cellvibrionales | Cellvibrionaceae | Cellvibrio | NA |
| ASV431 | Bacteria | Proteobacteria | Gammaproteobacteria | Alteromonadales | Alteromonadaceae | Glaciecola | NA |
| ASV258 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | NA |
| ASV3377 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | NA |
| ASV335 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | NA |
| ASV1488 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | NA |
| ASV2930 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Pseudomonadaceae | Pseudomonas | NA |
| ASV39 | Bacteria | Proteobacteria | Gammaproteobacteria | Cellvibrionales | Cellvibrionaceae | NA | NA |
| ASV1112 | Bacteria | Proteobacteria | Gammaproteobacteria | Gammaproteobacteria_Incertae_Sedis | Unknown_Family | Acidibacter | NA |
| ASV105 | Bacteria | Proteobacteria | Gammaproteobacteria | Burkholderiales | Comamonadaceae | Delftia | NA |
| ASV2151 | Bacteria | Proteobacteria | Gammaproteobacteria | Burkholderiales | Comamonadaceae | Acidovorax | NA |
| ASV186 | Bacteria | Proteobacteria | Gammaproteobacteria | Burkholderiales | Burkholderiaceae | Ralstonia | NA |
| ASV55 | Bacteria | Proteobacteria | Gammaproteobacteria | Burkholderiales | Alcaligenaceae | Achromobacter | NA |
| ASV214 | Bacteria | Proteobacteria | Gammaproteobacteria | Burkholderiales | Neisseriaceae | Neisseria | flavescens/perflava |
| ASV465 | Bacteria | Proteobacteria | Gammaproteobacteria | Enterobacterales | Yersiniaceae | Yersinia | NA |
| ASV81 | Bacteria | Proteobacteria | Gammaproteobacteria | Enterobacterales | Yersiniaceae | NA | NA |
| ASV36 | Bacteria | Proteobacteria | Gammaproteobacteria | Enterobacterales | Yersiniaceae | Serratia | NA |
| ASV7 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Moraxellaceae | Acinetobacter | NA |
| ASV198 | Bacteria | Proteobacteria | Gammaproteobacteria | Pseudomonadales | Moraxellaceae | Enhydrobacter | NA |
| ASV58 | Bacteria | Proteobacteria | Gammaproteobacteria | Xanthomonadales | Xanthomonadaceae | Stenotrophomonas | maltophilia |
| ASV2442 | Bacteria | Proteobacteria | Gammaproteobacteria | Xanthomonadales | Xanthomonadaceae | Stenotrophomonas | NA |
| ASV971 | Bacteria | Proteobacteria | Alphaproteobacteria | Caulobacterales | Hyphomonadaceae | NA | NA |
| ASV161 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Rhizobiaceae | Mesorhizobium | huakuii/loti |
| ASV326 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhizobiales | Rhizobiaceae | Pseudahrensia | NA |
| ASV428 | Bacteria | Proteobacteria | Alphaproteobacteria | Rhodobacterales | Rhodobacteraceae | Yoonia-Loktanella | NA |
Here’s a graph of the ASV prevalence in true vs negative control samples. Highlighted in blue are the ones identified as contaminants.
I also want to remove the chloroplasts from the samples. However, I want to make a record of the chloroplast abundance in the metadata of the sample, because it may be useful information later.
Removing chloroplasts, contaminants, and non-bacterial/archaeal ASVs How do the read counts look after we remove the chloroplast samples and the putative contaminants?
| skim_type | skim_variable | n_missing | complete_rate | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| numeric | nochim | 0 | 1 | 9.169762e+04 | 2.404930e+04 | 3.657100e+04 | 7.500600e+04 | 9.317500e+04 | 1.059280e+05 | 2.149200e+05 | ▂▇▃▁▁ |
| numeric | prop.retained | 0 | 1 | 7.731884e-01 | 7.250860e-02 | 4.670639e-01 | 7.423043e-01 | 7.793883e-01 | 8.306927e-01 | 8.716865e-01 | ▁▁▁▆▇ |
| numeric | reads.clean | 0 | 1 | 8.481864e+04 | 2.627104e+04 | 6.412000e+03 | 6.941350e+04 | 8.812500e+04 | 9.889350e+04 | 2.146840e+05 | ▂▇▇▁▁ |
| numeric | final.prop | 0 | 1 | 7.116660e-01 | 1.197761e-01 | 5.979110e-02 | 6.905133e-01 | 7.497528e-01 | 7.826109e-01 | 8.477940e-01 | ▁▁▁▂▇ |
The script preprocessing.R will generate the cleaned data that will be used in the subsequent analysis. After this, I will load the preprocessed .rds files
Comparing wild and F2 killifish alpha diversity. Black dots represent the water at each site at the time of sampling. Water for the captive killifish is not available.
### Alpha diversity significance tests
Pairwise t-tests show that NBH wild fish have significantly different diversity values than all other fish types while SC wild, SC F2, and NBH F2 fish have similar measurements. One exception is that SC wild has a higher Simpson measurement than NBH F2.
Subsetting to just the gut samples, now we look at the beta dispersion/diversity between sites and between F2 and wild type
Tukey’s HSD test shows that all pairwise comparisons of beta dispersion between fish types are significantly different except for between the F2 fish.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr
## New Bedford Harbor wild-New Bedford Harbor F2 0.53786755 0.45527199
## Scorton Creek F2-New Bedford Harbor F2 0.03212203 -0.08091673
## Scorton Creek wild-New Bedford Harbor F2 0.27806619 0.19547063
## Scorton Creek F2-New Bedford Harbor wild -0.50574551 -0.62021219
## Scorton Creek wild-New Bedford Harbor wild -0.25980136 -0.34434061
## Scorton Creek wild-Scorton Creek F2 0.24594415 0.13147747
## upr p adj
## New Bedford Harbor wild-New Bedford Harbor F2 0.6204631 0.0000000
## Scorton Creek F2-New Bedford Harbor F2 0.1451608 0.8810824
## Scorton Creek wild-New Bedford Harbor F2 0.3606617 0.0000000
## Scorton Creek F2-New Bedford Harbor wild -0.3912788 0.0000000
## Scorton Creek wild-New Bedford Harbor wild -0.1752621 0.0000000
## Scorton Creek wild-Scorton Creek F2 0.3604108 0.0000007
I attempted to do an NMDS ordination but metaMDS would not converge. So I created a PCoA using Bray-Curtis dissimilarity
## Run 0 stress 0.208238
## Run 1 stress 0.2261095
## Run 2 stress 0.2056419
## ... New best solution
## ... Procrustes: rmse 0.0547709 max resid 0.1872937
## Run 3 stress 0.2372614
## Run 4 stress 0.2203528
## Run 5 stress 0.2131
## Run 6 stress 0.2379258
## Run 7 stress 0.2094832
## Run 8 stress 0.2118012
## Run 9 stress 0.2248832
## Run 10 stress 0.2125689
## Run 11 stress 0.2300907
## Run 12 stress 0.2044313
## ... New best solution
## ... Procrustes: rmse 0.03024335 max resid 0.2080384
## Run 13 stress 0.2300912
## Run 14 stress 0.2093983
## Run 15 stress 0.2072195
## Run 16 stress 0.2084545
## Run 17 stress 0.232153
## Run 18 stress 0.2284961
## Run 19 stress 0.2293324
## Run 20 stress 0.223839
## Run 21 stress 0.2076043
## Run 22 stress 0.2091727
## Run 23 stress 0.2391361
## Run 24 stress 0.2079366
## Run 25 stress 0.233219
## Run 26 stress 0.2268915
## Run 27 stress 0.2082458
## Run 28 stress 0.2290063
## Run 29 stress 0.2182759
## Run 30 stress 0.2187313
## Run 31 stress 0.2114497
## Run 32 stress 0.2351815
## Run 33 stress 0.2310773
## Run 34 stress 0.2114346
## Run 35 stress 0.2175095
## Run 36 stress 0.224134
## Run 37 stress 0.2265515
## Run 38 stress 0.2129492
## Run 39 stress 0.2305793
## Run 40 stress 0.2065916
## Run 41 stress 0.2359861
## Run 42 stress 0.2367777
## Run 43 stress 0.2105909
## Run 44 stress 0.2324912
## Run 45 stress 0.2090001
## Run 46 stress 0.2090006
## Run 47 stress 0.2202035
## Run 48 stress 0.2130807
## Run 49 stress 0.2229764
## Run 50 stress 0.2279571
## Run 51 stress 0.2147011
## Run 52 stress 0.2081767
## Run 53 stress 0.2131402
## Run 54 stress 0.2082825
## Run 55 stress 0.2084623
## Run 56 stress 0.2124183
## Run 57 stress 0.2128082
## Run 58 stress 0.2195641
## Run 59 stress 0.2116707
## Run 60 stress 0.2122469
## Run 61 stress 0.2096514
## Run 62 stress 0.2346297
## Run 63 stress 0.2160481
## Run 64 stress 0.2247876
## Run 65 stress 0.2325831
## Run 66 stress 0.2039405
## ... New best solution
## ... Procrustes: rmse 0.0108751 max resid 0.06439859
## Run 67 stress 0.2086146
## Run 68 stress 0.225936
## Run 69 stress 0.228488
## Run 70 stress 0.2275537
## Run 71 stress 0.2085337
## Run 72 stress 0.2088519
## Run 73 stress 0.2340167
## Run 74 stress 0.2297894
## Run 75 stress 0.237668
## Run 76 stress 0.2092143
## Run 77 stress 0.2402769
## Run 78 stress 0.2202073
## Run 79 stress 0.2262458
## Run 80 stress 0.2085625
## Run 81 stress 0.2350829
## Run 82 stress 0.2076867
## Run 83 stress 0.225624
## Run 84 stress 0.2098127
## Run 85 stress 0.2235664
## Run 86 stress 0.2126822
## Run 87 stress 0.2066878
## Run 88 stress 0.2083945
## Run 89 stress 0.2074072
## Run 90 stress 0.2329584
## Run 91 stress 0.232984
## Run 92 stress 0.2352215
## Run 93 stress 0.23287
## Run 94 stress 0.207141
## Run 95 stress 0.2073268
## Run 96 stress 0.2071291
## Run 97 stress 0.2286822
## Run 98 stress 0.2257995
## Run 99 stress 0.2130298
## Run 100 stress 0.2352324
## Run 101 stress 0.2089648
## Run 102 stress 0.2087614
## Run 103 stress 0.2042002
## ... Procrustes: rmse 0.01232162 max resid 0.06891814
## Run 104 stress 0.2084149
## Run 105 stress 0.2112933
## Run 106 stress 0.2126853
## Run 107 stress 0.2414329
## Run 108 stress 0.2234091
## Run 109 stress 0.2049084
## Run 110 stress 0.2039405
## ... New best solution
## ... Procrustes: rmse 5.19067e-05 max resid 0.0002293289
## ... Similar to previous best
## *** Solution reached
Interestingly a PERMANOVA finds that the two F2 populations are significantly different
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = distance(ps.wild.rel, "bray") ~ site + weight.g + length.cm, data = as(sample_data(ps.wild.rel), "data.frame"), by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## site 1 5.3728 0.20073 19.8463 0.001 ***
## weight.g 1 0.4256 0.01590 1.5722 0.109
## length.cm 1 0.3991 0.01491 1.4742 0.137
## Residual 76 20.5748 0.76868
## Total 79 26.7663 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = distance(ps.f2.rel, "bray") ~ site + weight.g + length.cm + sex, data = as(sample_data(ps.f2.rel), "data.frame"), by = "margin")
## Df SumOfSqs R2 F Pr(>F)
## site 1 0.05604 0.07898 5.2105 0.003 **
## weight.g 1 0.01619 0.02282 1.5054 0.203
## length.cm 1 0.01157 0.01631 1.0760 0.342
## sex 1 0.00953 0.01344 0.8865 0.469
## Residual 54 0.58074 0.81856
## Total 58 0.70946 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## <ggproto object: Class CoordFixed, CoordCartesian, Coord, gg>
## aspect: function
## backtransform_range: function
## clip: on
## default: FALSE
## distance: function
## expand: TRUE
## is_free: function
## is_linear: function
## labels: function
## limits: list
## modify_scales: function
## range: function
## ratio: 0.637730270344354
## render_axis_h: function
## render_axis_v: function
## render_bg: function
## render_fg: function
## setup_data: function
## setup_layout: function
## setup_panel_guides: function
## setup_panel_params: function
## setup_params: function
## train_panel_guides: function
## transform: function
## super: <ggproto object: Class CoordFixed, CoordCartesian, Coord, gg>
within the wild samples, ASV2, ASV3, and ASV5 structure the populations
I was curious how many ASVs were unique to each environment/fish type: Scorton Creek wild, New Bedford Harbor wild, and the respective F2 fish
## [1] 1
Venn Diagram
F2 fish share 20% of their ASVs while wild fish share 6.3% of their ASVs.
Core taxa in wild type are either Vibrionaceae or Mycoplasma while core taxa in F2 are Lactobacillales
Core Lactobacillales ASVs:
| Order | Family | Genus | Species | |
|---|---|---|---|---|
| ASV1 | Lactobacillales | Enterococcaceae | Enterococcus | NA |
| ASV14 | Lactobacillales | Streptococcaceae | Lactococcus | formosensis/garvieae |
| ASV16 | Lactobacillales | Leuconostocaceae | Weissella | NA |
| ASV45 | Lactobacillales | Streptococcaceae | Lactococcus | garvieae/lactis |
| ASV1881 | Lactobacillales | Leuconostocaceae | Leuconostoc | NA |
| Order | Family | Genus | Species | |
|---|---|---|---|---|
| ASV2 | Vibrionales | Vibrionaceae | NA | NA |
| ASV5 | Vibrionales | Vibrionaceae | Vibrio | NA |
| ASV13 | Vibrionales | Vibrionaceae | Vibrio | NA |
| ASV18 | Vibrionales | Vibrionaceae | Aliivibrio | NA |
| ASV43 | Vibrionales | Vibrionaceae | Photobacterium | NA |
| Order | Family | Genus | Species | |
|---|---|---|---|---|
| ASV3 | Mycoplasmatales | Mycoplasmataceae | Mycoplasma | NA |
| ASV10 | Mycoplasmatales | Mycoplasmataceae | NA | NA |
| ASV26 | Mycoplasmatales | Mycoplasmataceae | Mycoplasma | NA |
There are still some of these major groups that are not part of the core that are super abundant in some samples. The following bar chart includes all ASVs from the three main orders, not just the core ASVs. Notice the Lactobacillales that appear in a few of the NBH wild fish and the additional Vibrionales in the SC wild fish.
I wanted to know what were the most dominant orders in each fish type. So I found the top 5 average relative abundance of order for each fish type and plotted their abundances. 15 Orders had high average relative abundance in at least one fish type, but some were clearly due to a couple of outlier fish.
I removed those orders in which only a few fish showed high relative abundance. Now the number of orders is down to 5 and it’s much easier to interpret. Wild fish are dominated by Vibrio and Mycoplasmatales while F2 fish are dominated by Lactobacillales and somewhat by Clostridiales and Cyanobacteriales. I found it interesting that SC wild fish have huge amounts to Vibrio but also huge amounts of Myco, while NBH wild just has a variable amount of vibrio and a small amount of myco. I wonder if the Myco and Vibrio in SC are negatively correlated. For more, see the vibrio section.
TBD
Differentially abundant ASVs between wild and F2
Differentially abundant taxa between F2 SC and NBH fish
Differentially abundant ASVs between NBH wild and SC wild
| ASV | SC_enriched | Taxonomy | NBH_abund | SC_abund | expected |
|---|---|---|---|---|---|
| ASV104 | FALSE | g:Cyanobium_PCC-6307 | 0.0039700 | 0.0424259 | no |
| ASV133 | FALSE | f:Intrasporangiaceae | 0.0000000 | 0.0000000 | no |
| ASV145 | FALSE | g:Exiguobacterium | 0.0000000 | 0.0000000 | no |
| ASV17 | FALSE | f:Vibrionaceae | 0.0000000 | 0.0000000 | no |
| ASV2 | TRUE | f:Vibrionaceae | 0.0014670 | 0.0013125 | no |
| ASV201 | FALSE | Ahrensia kielensis/marina | 0.0000000 | 0.0000000 | no |
| ASV221 | FALSE | Sulfitobacter dubius | 0.0000000 | 0.0000000 | no |
| ASV225 | FALSE | Labrenzia aggregata | 0.0000000 | 0.0000000 | no |
| ASV227 | FALSE | Tropicimonas aquimaris | 0.0000000 | 0.0000000 | no |
| ASV23 | FALSE | g:Aliivibrio | 0.0000000 | 0.0000000 | no |
| ASV238 | FALSE | f:Rhizobiaceae | 0.0000000 | 0.0000000 | no |
| ASV24 | FALSE | f:Desulfovibrionaceae | 0.0000000 | 0.0000000 | no |
| ASV241 | FALSE | Ilumatobacter nonamiensis | 0.0000000 | 0.0000000 | no |
| ASV26 | TRUE | g:Mycoplasma | 0.0000000 | 0.0000000 | no |
| ASV261 | FALSE | f:Nitriliruptoraceae | 0.0000000 | 0.0000000 | no |
| ASV28 | FALSE | Psychromonas kaikoae/marina | 0.0000000 | 0.0012025 | no |
| ASV33 | FALSE | g:Brevinema | 0.0000000 | 0.0000000 | no |
| ASV43 | FALSE | g:Photobacterium | 0.0003281 | 0.0008345 | no |
| ASV47 | FALSE | g:Candidatus_Bacilloplasma | 0.0000000 | 0.0000000 | no |
| ASV5 | FALSE | g:Vibrio | 0.0031767 | 0.0037468 | no |
| ASV503 | FALSE | g:Planococcus | 0.0000000 | 0.0000000 | no |
| ASV513 | FALSE | f:Caldilineaceae | 0.0000000 | 0.0000000 | no |
| ASV52 | FALSE | g:Ascidiaceihabitans | 0.0000000 | 0.0000000 | no |
| ASV616 | FALSE | c:KD4-96 | 0.0000000 | 0.0000000 | no |
| ASV67 | FALSE | Yangia pacifica | 0.0000000 | 0.0000000 | no |
| ASV79 | TRUE | g:Propionigenium | 0.0000000 | 0.0000000 | no |
| ASV82 | FALSE | g:Rubripirellula | 0.0000000 | 0.0003079 | no |
| ASV91 | FALSE | Enterococcus cecorum | 0.0000000 | 0.0000000 | no |
| ASV10 | TRUE | f:Mycoplasmataceae | 0.0000000 | 0.0000494 | yes |
| ASV106 | FALSE | c:Gammaproteobacteria | 0.0004223 | 0.0000000 | yes |
| ASV113 | FALSE | g:Sulfurovum | 0.0004773 | 0.0000000 | yes |
| ASV22 | FALSE | Lactobacillus intermedius | 0.0001000 | 0.0000000 | yes |
| ASV229 | FALSE | f:Gimesiaceae | 0.0003310 | 0.0000000 | yes |
| ASV246 | FALSE | g:Halioglobus | 0.0008910 | 0.0000000 | yes |
| ASV27 | TRUE | g:Marinobacterium | 0.0000000 | 0.0008759 | yes |
| ASV30 | FALSE | g:Marivita | 0.0001448 | 0.0000000 | yes |
| ASV328 | FALSE | g:Methyloceanibacter | 0.0000521 | 0.0000000 | yes |
| ASV64 | FALSE | f:Desulfocapsaceae | 0.0006620 | 0.0000000 | yes |
| ASV80 | FALSE | g:Roseobacter | 0.0001043 | 0.0000000 | yes |
| ASV85 | TRUE | g:Thiohalocapsa | 0.0000000 | 0.0003383 | yes |
In the core taxa section, I found that Vibrionales is highly abundant in wild fish, and particularly in Scorton Creek wild fish. In the differential abundance analysis, I found that it’s a specific vibrio ASV (ASV2) that is enriched in SC fish (both wild and F2) while NBH wild fish are enriched in different vibrio taxa. I think this difference warrants further investigation.
I looked at the overlap in vibrio ASVs between the two fish populations.
Scorton Creek fish have 77 Vibrio ASVs, New Bedford fish have 115 Vibrio ASVs, and they share 41 Vibrio ASVs. For clarity, I only plotted the ASVs which ever reach a relative abundance above 1%. In SC ASV2 is dominant, but in NBH ASV2 and ASV5 are about equally dominant
## Vibrio corrplot
TBD
I found that SC fish had high abundances of both vibrios and mycoplasmatales and wondered if they were negatively correlated overall and if Vibrio and Mycoplasmatales collectively structure wild (or just SC) gut microbiomes.
Let’s look at the distribution of host genotypes. I think it’s interesting that the distribution of genotypes is so similar for the wild fish but different in the F2 that were common garden raised. Fish with genotype “none” did not have a band wither either set of primer. I have re-extracted DNA and re-run the primers for the wild fish, which decreased the number of “none” genotypes, but have not done so yet for the F2 fish, hence the greater number of “none” fish in F2.
Not looking great visually
## Run 0 stress 0.2094239
## Run 1 stress 0.2157774
## Run 2 stress 0.2260074
## Run 3 stress 0.2340675
## Run 4 stress 0.2146944
## Run 5 stress 0.2385085
## Run 6 stress 0.2081324
## ... New best solution
## ... Procrustes: rmse 0.03943267 max resid 0.2098362
## Run 7 stress 0.2296754
## Run 8 stress 0.2104472
## Run 9 stress 0.2103757
## Run 10 stress 0.2148912
## Run 11 stress 0.2079386
## ... New best solution
## ... Procrustes: rmse 0.02424882 max resid 0.1425916
## Run 12 stress 0.2383765
## Run 13 stress 0.2273198
## Run 14 stress 0.2125026
## Run 15 stress 0.2107662
## Run 16 stress 0.2359099
## Run 17 stress 0.2101845
## Run 18 stress 0.2136559
## Run 19 stress 0.230167
## Run 20 stress 0.2211825
## Run 21 stress 0.2106684
## Run 22 stress 0.2114368
## Run 23 stress 0.2315511
## Run 24 stress 0.2248752
## Run 25 stress 0.2169388
## Run 26 stress 0.2081224
## ... Procrustes: rmse 0.01327129 max resid 0.06675177
## Run 27 stress 0.2096796
## Run 28 stress 0.2133297
## Run 29 stress 0.213637
## Run 30 stress 0.2462523
## Run 31 stress 0.2299241
## Run 32 stress 0.2265549
## Run 33 stress 0.2257488
## Run 34 stress 0.2239966
## Run 35 stress 0.205346
## ... New best solution
## ... Procrustes: rmse 0.05798644 max resid 0.1929456
## Run 36 stress 0.2094419
## Run 37 stress 0.2310053
## Run 38 stress 0.2393703
## Run 39 stress 0.2055215
## ... Procrustes: rmse 0.01202388 max resid 0.06646121
## Run 40 stress 0.2135558
## Run 41 stress 0.2183995
## Run 42 stress 0.2282661
## Run 43 stress 0.2246887
## Run 44 stress 0.2382327
## Run 45 stress 0.2077069
## Run 46 stress 0.2362343
## Run 47 stress 0.2363008
## Run 48 stress 0.2051982
## ... New best solution
## ... Procrustes: rmse 0.008310794 max resid 0.04943289
## Run 49 stress 0.2127829
## Run 50 stress 0.2109343
## Run 51 stress 0.2417562
## Run 52 stress 0.2330226
## Run 53 stress 0.2264774
## Run 54 stress 0.2237656
## Run 55 stress 0.2470312
## Run 56 stress 0.2254736
## Run 57 stress 0.224659
## Run 58 stress 0.2123271
## Run 59 stress 0.2303092
## Run 60 stress 0.2064145
## Run 61 stress 0.2379212
## Run 62 stress 0.2089927
## Run 63 stress 0.2055712
## ... Procrustes: rmse 0.01067262 max resid 0.07392896
## Run 64 stress 0.2408523
## Run 65 stress 0.2311619
## Run 66 stress 0.2111465
## Run 67 stress 0.2051091
## ... New best solution
## ... Procrustes: rmse 0.01072919 max resid 0.05670056
## Run 68 stress 0.2456645
## Run 69 stress 0.2207193
## Run 70 stress 0.2143196
## Run 71 stress 0.2217399
## Run 72 stress 0.2265254
## Run 73 stress 0.2349678
## Run 74 stress 0.2193564
## Run 75 stress 0.2367495
## Run 76 stress 0.2171829
## Run 77 stress 0.2130117
## Run 78 stress 0.2218182
## Run 79 stress 0.241517
## Run 80 stress 0.2296554
## Run 81 stress 0.2104327
## Run 82 stress 0.2311274
## Run 83 stress 0.2328461
## Run 84 stress 0.2142026
## Run 85 stress 0.2243672
## Run 86 stress 0.2101052
## Run 87 stress 0.2284881
## Run 88 stress 0.2263735
## Run 89 stress 0.220634
## Run 90 stress 0.2123613
## Run 91 stress 0.2314886
## Run 92 stress 0.2439834
## Run 93 stress 0.2101491
## Run 94 stress 0.2094436
## Run 95 stress 0.2434325
## Run 96 stress 0.211102
## Run 97 stress 0.2123345
## Run 98 stress 0.2135842
## Run 99 stress 0.2261107
## Run 100 stress 0.2061835
## Run 101 stress 0.2224873
## Run 102 stress 0.2090026
## Run 103 stress 0.2405005
## Run 104 stress 0.2308013
## Run 105 stress 0.2264014
## Run 106 stress 0.2401571
## Run 107 stress 0.2151815
## Run 108 stress 0.2058128
## Run 109 stress 0.2049708
## ... New best solution
## ... Procrustes: rmse 0.009932977 max resid 0.07604792
## Run 110 stress 0.2366083
## Run 111 stress 0.2057106
## Run 112 stress 0.2418679
## Run 113 stress 0.2065009
## Run 114 stress 0.2083755
## Run 115 stress 0.2316151
## Run 116 stress 0.2238095
## Run 117 stress 0.213772
## Run 118 stress 0.213625
## Run 119 stress 0.2469097
## Run 120 stress 0.2121885
## Run 121 stress 0.2258479
## Run 122 stress 0.2116042
## Run 123 stress 0.209135
## Run 124 stress 0.2087854
## Run 125 stress 0.2076727
## Run 126 stress 0.2131979
## Run 127 stress 0.2051982
## ... Procrustes: rmse 0.008986036 max resid 0.05684277
## Run 128 stress 0.2389542
## Run 129 stress 0.2080353
## Run 130 stress 0.2086044
## Run 131 stress 0.2259291
## Run 132 stress 0.205109
## ... Procrustes: rmse 0.009946142 max resid 0.07617127
## Run 133 stress 0.2272624
## Run 134 stress 0.2140034
## Run 135 stress 0.2281391
## Run 136 stress 0.2231591
## Run 137 stress 0.2051091
## ... Procrustes: rmse 0.009933417 max resid 0.07600058
## Run 138 stress 0.2363222
## Run 139 stress 0.244955
## Run 140 stress 0.2049372
## ... New best solution
## ... Procrustes: rmse 0.005604074 max resid 0.03200545
## Run 141 stress 0.2121307
## Run 142 stress 0.2054607
## Run 143 stress 0.2087408
## Run 144 stress 0.2075435
## Run 145 stress 0.2142665
## Run 146 stress 0.2153982
## Run 147 stress 0.213823
## Run 148 stress 0.2134526
## Run 149 stress 0.23527
## Run 150 stress 0.2082867
## Run 151 stress 0.2061833
## Run 152 stress 0.2080253
## Run 153 stress 0.2420936
## Run 154 stress 0.215156
## Run 155 stress 0.2250262
## Run 156 stress 0.2331436
## Run 157 stress 0.2355074
## Run 158 stress 0.2089828
## Run 159 stress 0.21423
## Run 160 stress 0.2080441
## Run 161 stress 0.2289173
## Run 162 stress 0.2389676
## Run 163 stress 0.2121632
## Run 164 stress 0.2305304
## Run 165 stress 0.2362499
## Run 166 stress 0.223972
## Run 167 stress 0.212958
## Run 168 stress 0.2125874
## Run 169 stress 0.2105423
## Run 170 stress 0.2061777
## Run 171 stress 0.2321443
## Run 172 stress 0.213356
## Run 173 stress 0.2247826
## Run 174 stress 0.2290385
## Run 175 stress 0.2239726
## Run 176 stress 0.2086436
## Run 177 stress 0.215454
## Run 178 stress 0.2071893
## Run 179 stress 0.208864
## Run 180 stress 0.2056064
## Run 181 stress 0.2065009
## Run 182 stress 0.2438026
## Run 183 stress 0.2372219
## Run 184 stress 0.2051592
## ... Procrustes: rmse 0.007769127 max resid 0.05735283
## Run 185 stress 0.2093274
## Run 186 stress 0.2137622
## Run 187 stress 0.2064274
## Run 188 stress 0.232173
## Run 189 stress 0.2052271
## ... Procrustes: rmse 0.008528714 max resid 0.0572622
## Run 190 stress 0.2083337
## Run 191 stress 0.2067813
## Run 192 stress 0.2377806
## Run 193 stress 0.2074037
## Run 194 stress 0.2061733
## Run 195 stress 0.2371606
## Run 196 stress 0.2442204
## Run 197 stress 0.2394952
## Run 198 stress 0.2311109
## Run 199 stress 0.2101553
## Run 200 stress 0.2246602
## Run 201 stress 0.2294761
## Run 202 stress 0.2402689
## Run 203 stress 0.2054932
## Run 204 stress 0.2306398
## Run 205 stress 0.2299493
## Run 206 stress 0.2312014
## Run 207 stress 0.2349412
## Run 208 stress 0.2049352
## ... New best solution
## ... Procrustes: rmse 0.003688029 max resid 0.02267798
## Run 209 stress 0.2326277
## Run 210 stress 0.2439299
## Run 211 stress 0.2079659
## Run 212 stress 0.2168838
## Run 213 stress 0.2200503
## Run 214 stress 0.2368714
## Run 215 stress 0.2316767
## Run 216 stress 0.2232205
## Run 217 stress 0.2455869
## Run 218 stress 0.2194556
## Run 219 stress 0.2414474
## Run 220 stress 0.2105457
## Run 221 stress 0.2289407
## Run 222 stress 0.244274
## Run 223 stress 0.2151952
## Run 224 stress 0.2306842
## Run 225 stress 0.2153257
## Run 226 stress 0.2139287
## Run 227 stress 0.2323504
## Run 228 stress 0.2158736
## Run 229 stress 0.2096629
## Run 230 stress 0.2136318
## Run 231 stress 0.2067735
## Run 232 stress 0.2088582
## Run 233 stress 0.2256605
## Run 234 stress 0.2366392
## Run 235 stress 0.2096705
## Run 236 stress 0.2086463
## Run 237 stress 0.2377577
## Run 238 stress 0.2166291
## Run 239 stress 0.2259506
## Run 240 stress 0.2182905
## Run 241 stress 0.2368364
## Run 242 stress 0.2101351
## Run 243 stress 0.2359625
## Run 244 stress 0.2391847
## Run 245 stress 0.2106997
## Run 246 stress 0.2365985
## Run 247 stress 0.2110305
## Run 248 stress 0.2388789
## Run 249 stress 0.2052727
## ... Procrustes: rmse 0.01003568 max resid 0.06911455
## Run 250 stress 0.2371073
## Run 251 stress 0.2140436
## Run 252 stress 0.2439886
## Run 253 stress 0.2064275
## Run 254 stress 0.2300807
## Run 255 stress 0.2369869
## Run 256 stress 0.2360284
## Run 257 stress 0.2062234
## Run 258 stress 0.2315481
## Run 259 stress 0.230453
## Run 260 stress 0.2261407
## Run 261 stress 0.2078695
## Run 262 stress 0.2147679
## Run 263 stress 0.2263779
## Run 264 stress 0.2391057
## Run 265 stress 0.2270544
## Run 266 stress 0.2330683
## Run 267 stress 0.2265041
## Run 268 stress 0.2331798
## Run 269 stress 0.2123614
## Run 270 stress 0.209969
## Run 271 stress 0.2159813
## Run 272 stress 0.2228965
## Run 273 stress 0.2064145
## Run 274 stress 0.205346
## ... Procrustes: rmse 0.00961791 max resid 0.05734518
## Run 275 stress 0.2354981
## Run 276 stress 0.2279352
## Run 277 stress 0.2101765
## Run 278 stress 0.2103134
## Run 279 stress 0.2151676
## Run 280 stress 0.2407643
## Run 281 stress 0.2134605
## Run 282 stress 0.2101562
## Run 283 stress 0.2093177
## Run 284 stress 0.2286462
## Run 285 stress 0.2115182
## Run 286 stress 0.2065584
## Run 287 stress 0.2278567
## Run 288 stress 0.2084341
## Run 289 stress 0.2119909
## Run 290 stress 0.2067651
## Run 291 stress 0.2076995
## Run 292 stress 0.228722
## Run 293 stress 0.2222122
## Run 294 stress 0.2292178
## Run 295 stress 0.2318521
## Run 296 stress 0.2143546
## Run 297 stress 0.2076629
## Run 298 stress 0.2463396
## Run 299 stress 0.2455455
## Run 300 stress 0.2389751
## Run 301 stress 0.2088411
## Run 302 stress 0.2311292
## Run 303 stress 0.2101996
## Run 304 stress 0.2339335
## Run 305 stress 0.2302411
## Run 306 stress 0.2133356
## Run 307 stress 0.2256722
## Run 308 stress 0.2116705
## Run 309 stress 0.2406451
## Run 310 stress 0.205109
## ... Procrustes: rmse 0.006870954 max resid 0.04731704
## Run 311 stress 0.2272904
## Run 312 stress 0.205109
## ... Procrustes: rmse 0.00687886 max resid 0.04744695
## Run 313 stress 0.2051597
## ... Procrustes: rmse 0.007793126 max resid 0.04712099
## Run 314 stress 0.2141428
## Run 315 stress 0.2332293
## Run 316 stress 0.2085
## Run 317 stress 0.2132155
## Run 318 stress 0.2108137
## Run 319 stress 0.2154058
## Run 320 stress 0.2106114
## Run 321 stress 0.2076353
## Run 322 stress 0.2326997
## Run 323 stress 0.2301842
## Run 324 stress 0.2094551
## Run 325 stress 0.206925
## Run 326 stress 0.2131202
## Run 327 stress 0.2116249
## Run 328 stress 0.2092726
## Run 329 stress 0.2101594
## Run 330 stress 0.2055131
## Run 331 stress 0.2123732
## Run 332 stress 0.2095269
## Run 333 stress 0.2431066
## Run 334 stress 0.2442961
## Run 335 stress 0.2064145
## Run 336 stress 0.2142442
## Run 337 stress 0.23795
## Run 338 stress 0.2096996
## Run 339 stress 0.2376278
## Run 340 stress 0.243288
## Run 341 stress 0.2136044
## Run 342 stress 0.2112818
## Run 343 stress 0.226314
## Run 344 stress 0.2192498
## Run 345 stress 0.2121882
## Run 346 stress 0.2236008
## Run 347 stress 0.2051678
## ... Procrustes: rmse 0.006828148 max resid 0.04837384
## Run 348 stress 0.2307428
## Run 349 stress 0.2442202
## Run 350 stress 0.2209855
## Run 351 stress 0.2087844
## Run 352 stress 0.2363155
## Run 353 stress 0.2100822
## Run 354 stress 0.2123063
## Run 355 stress 0.2196179
## Run 356 stress 0.2094953
## Run 357 stress 0.2093195
## Run 358 stress 0.240378
## Run 359 stress 0.242381
## Run 360 stress 0.2062572
## Run 361 stress 0.2094674
## Run 362 stress 0.2374466
## Run 363 stress 0.2051091
## ... Procrustes: rmse 0.006878626 max resid 0.04743889
## Run 364 stress 0.2097124
## Run 365 stress 0.2159819
## Run 366 stress 0.2105228
## Run 367 stress 0.2394005
## Run 368 stress 0.2061732
## Run 369 stress 0.2254161
## Run 370 stress 0.2368589
## Run 371 stress 0.2236687
## Run 372 stress 0.2157175
## Run 373 stress 0.2272161
## Run 374 stress 0.2250152
## Run 375 stress 0.2055175
## Run 376 stress 0.2251803
## Run 377 stress 0.2251491
## Run 378 stress 0.2283529
## Run 379 stress 0.2126357
## Run 380 stress 0.2406926
## Run 381 stress 0.223573
## Run 382 stress 0.2401881
## Run 383 stress 0.2360507
## Run 384 stress 0.207266
## Run 385 stress 0.2092853
## Run 386 stress 0.2128868
## Run 387 stress 0.2055214
## Run 388 stress 0.2339978
## Run 389 stress 0.2136176
## Run 390 stress 0.2084914
## Run 391 stress 0.211325
## Run 392 stress 0.2370026
## Run 393 stress 0.2315139
## Run 394 stress 0.2218907
## Run 395 stress 0.2396513
## Run 396 stress 0.2063306
## Run 397 stress 0.2088178
## Run 398 stress 0.208361
## Run 399 stress 0.2158339
## Run 400 stress 0.2098593
## Run 401 stress 0.2275321
## Run 402 stress 0.2407526
## Run 403 stress 0.2104139
## Run 404 stress 0.2425808
## Run 405 stress 0.2257607
## Run 406 stress 0.2246578
## Run 407 stress 0.2068981
## Run 408 stress 0.2342482
## Run 409 stress 0.2301335
## Run 410 stress 0.2329696
## Run 411 stress 0.2136081
## Run 412 stress 0.2396381
## Run 413 stress 0.2089944
## Run 414 stress 0.2337164
## Run 415 stress 0.213777
## Run 416 stress 0.2079855
## Run 417 stress 0.215946
## Run 418 stress 0.2080502
## Run 419 stress 0.2277592
## Run 420 stress 0.2152816
## Run 421 stress 0.2079431
## Run 422 stress 0.2352768
## Run 423 stress 0.2270459
## Run 424 stress 0.2057165
## Run 425 stress 0.214811
## Run 426 stress 0.2147054
## Run 427 stress 0.2252102
## Run 428 stress 0.2074843
## Run 429 stress 0.2414707
## Run 430 stress 0.2260833
## Run 431 stress 0.2173618
## Run 432 stress 0.2281661
## Run 433 stress 0.2305255
## Run 434 stress 0.2090583
## Run 435 stress 0.2361227
## Run 436 stress 0.2051091
## ... Procrustes: rmse 0.006879076 max resid 0.0474466
## Run 437 stress 0.209293
## Run 438 stress 0.2378095
## Run 439 stress 0.2072412
## Run 440 stress 0.2357206
## Run 441 stress 0.2366507
## Run 442 stress 0.2247267
## Run 443 stress 0.2094981
## Run 444 stress 0.2334398
## Run 445 stress 0.2089173
## Run 446 stress 0.2086638
## Run 447 stress 0.2125608
## Run 448 stress 0.2277668
## Run 449 stress 0.2078285
## Run 450 stress 0.2288402
## Run 451 stress 0.22752
## Run 452 stress 0.2067795
## Run 453 stress 0.237524
## Run 454 stress 0.2146691
## Run 455 stress 0.206898
## Run 456 stress 0.2083548
## Run 457 stress 0.2289782
## Run 458 stress 0.2102644
## Run 459 stress 0.2253871
## Run 460 stress 0.2306201
## Run 461 stress 0.2065009
## Run 462 stress 0.2122673
## Run 463 stress 0.2105119
## Run 464 stress 0.2090584
## Run 465 stress 0.21165
## Run 466 stress 0.2403189
## Run 467 stress 0.2297095
## Run 468 stress 0.2347849
## Run 469 stress 0.2083297
## Run 470 stress 0.2238213
## Run 471 stress 0.2086127
## Run 472 stress 0.2085213
## Run 473 stress 0.2333247
## Run 474 stress 0.2138695
## Run 475 stress 0.2065009
## Run 476 stress 0.2090532
## Run 477 stress 0.2093304
## Run 478 stress 0.2153754
## Run 479 stress 0.2264083
## Run 480 stress 0.2315219
## Run 481 stress 0.234165
## Run 482 stress 0.2326138
## Run 483 stress 0.208678
## Run 484 stress 0.2380717
## Run 485 stress 0.2268063
## Run 486 stress 0.2114783
## Run 487 stress 0.2123347
## Run 488 stress 0.2264854
## Run 489 stress 0.2334978
## Run 490 stress 0.2384193
## Run 491 stress 0.2375271
## Run 492 stress 0.2174159
## Run 493 stress 0.2095724
## Run 494 stress 0.2303759
## Run 495 stress 0.2080237
## Run 496 stress 0.2090355
## Run 497 stress 0.2076158
## Run 498 stress 0.2100446
## Run 499 stress 0.2442039
## Run 500 stress 0.2054774
## *** No convergence -- monoMDS stopping criteria:
## 34: no. of iterations >= maxit
## 462: stress ratio > sratmax
## 4: scale factor of the gradient < sfgrmin
Permanova confirms no correlation
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = p.bc ~ genotype, data = as(sample_data(ps.rel.filtered), "data.frame"))
## Df SumOfSqs R2 F Pr(>F)
## genotype 2 0.506 0.01975 0.7356 0.818
## Residual 73 25.111 0.98025
## Total 75 25.617 1.00000